105 citations · 167 across the 25 of their papers we have counts for
25 papers
Description and Discussion on DCASE 2026 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Binh Thien Nguyen, Masahiro Yasuda, Noboru Harada +8
This paper presents an overview of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2026 Challenge Task 4, Spatial Semantic Segmentation of Sound Scenes (S5).…
Rethinking Masking Strategies for Masked Prediction-based Audio Self-supervised Learning
Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3
Since the introduction of Masked Autoencoders, various improvements to masking techniques have been explored. In this paper, we rethink masking strategies for audio representation…
Class-Aware Permutation-Invariant Signal-to-Distortion Ratio for Semantic Segmentation of Sound Scene with Same-Class Sources
Binh Thien Nguyen, Masahiro Yasuda, Daiki Takeuchi +2
To advance immersive communication, the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge recently introduced Task 4 on Spatial Semantic Segmentatio…
Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Masahiro Yasuda, Binh Thien Nguyen, Noboru Harada +10
Spatial Semantic Segmentation of Sound Scenes (S5) aims to enhance technologies for sound event detection and separation from multi-channel input signals that mix multiple sound ev…
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer
Daiki Takeuchi, Binh Thien Nguyen, Masahiro Yasuda +3
Automated Audio Captioning (AAC) aims to describe the semantic contexts of general sounds, including acoustic events and scenes, by leveraging effective acoustic features. To enhan…
Towards Pre-training an Effective Respiratory Audio Foundation Model
Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3
Recent advancements in foundation models have sparked interest in respiratory audio foundation models. However, the effectiveness of applying conventional pre-training schemes to d…
Assessing the Utility of Audio Foundation Models for Heart and Respiratory Sound Analysis
Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3
Pre-trained deep learning models, known as foundation models, have become essential building blocks in machine learning domains such as natural language processing and image domain…
M2D-CLAP: Exploring General-purpose Audio-Language Representations Beyond CLAP
Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda +3
Contrastive language-audio pre-training (CLAP), which learns audio-language representations by aligning audio and text in a common feature space, has become popular for solving aud…
Baseline Systems and Evaluation Metrics for Spatial Semantic Segmentation of Sound Scenes
Binh Thien Nguyen, Masahiro Yasuda, Daiki Takeuchi +3
Immersive communication has made significant advancements, especially with the release of the codec for Immersive Voice and Audio Services. Aiming at its further realization, the D…
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +4
Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio and exhibits promising performance in several classification tasks. However, conventional a…
Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Masahiro Yasuda, Noboru Harada, Yasunori Ohishi +3
Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-worl…
6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human
Masahiro Yasuda, Shoichiro Saito, Akira Nakayama +1
We aim to perform sound event localization and detection (SELD) using wearable equipment for a moving human, such as a pedestrian. Conventional SELD tasks have dealt only with micr…
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline
Noboru Harada, Daisuke Niizumi, Yasunori Ohishi +2
This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring. First-shot ASD does not allow systems to do ma…
Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion
Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito +1
We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microph…
Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments
Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito
Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degrad…
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
Kento Nagatomo, Masahiro Yasuda, Kohei Yatabe +2
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with t…
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization
Tomoro Tanaka, Kohei Yatabe, Masahiro Yasuda +1
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been de…
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
Noboru Harada, Daisuke Niizumi, Daiki Takeuchi +3
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS). As did for our previous ToyADMOS dataset, we collected a…
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval
Yuma Koizumi, Yasunori Ohishi, Daisuke Niizumi +2
The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to th…
A Transformer-based Audio Captioning Model with Keyword Estimation
Yuma Koizumi, Ryo Masumura, Kyosuke Nishida +2
One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be descr…
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto +8
In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitori…
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation
Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito +2
We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. comple…
Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels
Keisuke Imoto, Noriyuki Tonami, Yuma Koizumi +3
Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to…
DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector
Masahiro Yasuda, Yuma Koizumi, Luca Mazzon +2
We propose a direction of arrival (DOA) estimation method that combines sound-intensity vector (IV)-based DOA estimation and DNN-based denoising and dereverberation. Since the accu…